DEVOPS

High-Cardinality Field Bucketing Proposal via GitLab MR

Detects Honeycomb dimensions that should be bucketed rather than dropped (latency, payload size, unbounded paths).

CategoryDevOps
Enginesim
Difficultyadvanced
Triggerschedule
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerWeekly schedule starts the bucketing cleanup pass
  • ActionQuery Honeycomb for high-cardinality numeric and path fieldsHoneycomb
  • LogicClassify each candidate by bucketing strategy
  • ActionGenerate derived-column expression that buckets raw valuesHoneycomb
  • OutputOpen GitLab MR proposing the derived column and raw-field deprecationGitLabGitLab

What it does

Not every high-cardinality field is junk. Some carry signal but in raw form, like exact latency in milliseconds or request paths with embedded IDs. This workflow finds those, proposes a derived-column that buckets the values into a small set of ranges, and ships the change as a GitLab MR.

When to use it

Use it when dropping or sampling a field loses information you actually want, but the raw cardinality is killing query performance and cost. Bucketing keeps the insight at a fraction of the distinct-value count.

How it works

  1. 1A weekly schedule starts the cleanup pass.
  2. 2It queries Honeycomb for numeric and path-like dimensions with high cardinality and active query usage.
  3. 3A logic step classifies each candidate by bucketing strategy: range buckets for numerics, ID-stripping for paths.
  4. 4It generates the derived-column expression that maps raw values into the bucketed dimension.
  5. 5It opens a GitLab MR proposing the derived column alongside a plan to deprecate the raw field, with cardinality-reduction estimates.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect HoneycombDistributed traces and queries.
  2. 2
    Connect GitLabRepos, MRs, pipelines, registry.
  3. 3
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  4. 4
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  5. 5
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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